Web 3.0 in action: Vector Space Model for semantic (movie) Recommendations

Authors:
Roberto Mirizzi;Tommaso Di Noia;Eugenio Di Sciascio;Azzurra Ragone
Affiliations:
Politecnico di Bari, Bari, Italy;Politecnico di Bari, Bari, Italy;Politecnico di Bari, Bari, Italy;Exprivia S.p.A., Viale A. Olivetti, Molfetta (BA), Italy
Venue:
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Year:
2012

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Abstract

In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic similarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. MORE is freely available as a Facebook application.